Interested in this AI/ML Engineer role at Menarini?
Apply Now →Skills & Technologies
About This Role
Overview:
Position Title: IT AI Product Owner
Reports to: Head of AI Engineering
Our company is seeking an IT AI Product Owner to support the operation and continuous improvement of our AI\-enabled business solutions/products, with a specific focus on Generative Engine Optimization (GEO) — the discipline of ensuring company brands, products, and scientific content are accurately represented, discoverable, and favorably surfaced across AI\-driven search and answer engines (e.g., ChatGPT, Perplexity, Gemini, Copilot, and similar generative platforms). This role sits at the intersection of IT operations, and business function teams such as digital marketing/communications, and content governance, and requires someone comfortable working across technical systems, business stakeholders, and compliance requirements unique to the life sciences industry.
The ideal candidate will combine solid technical/business analysis skills with curiosity about the evolving generative AI search landscape, and will be comfortable owning a recurring monitoring cadence, translating findings into actionable recommendations, and partnering with Marketing, Medical/Regulatory, and IT teams to keep company content accurate and optimized. What Success Looks Like* AI solution incidents and enhancement requests are resolved efficiently, with strong stakeholder satisfaction.
- A consistent, well\-documented GEO monitoring cadence is in place, surfacing issues before they become reputational or compliance risks.
- Content updates driven by GEO insights are implemented on schedule and demonstrably improve the accuracy and visibility of company assets in AI\-generated answers.
- IT, Marketing, Medical, and Regulatory teams view this role as a trusted, proactive partner on AI and digital content matters.
Responsibilities:
Business Translation \& Agile Backlog Management:
- Translation of complex, unstructured business workflows and strategic goals into clear, actionable user stories, acceptance criteria, and system requirements that IT and engineering teams can immediately execute within an Agile/Scrum environment.
- Serve as day\-to\-day operational support for internal AI solutions/tools (e.g., generative AI writing assistants, chatbots, internal copilots, AI\-enabled analytics platforms), monitoring performance, availability, and adoption.
- Triage and resolve or escalate incidents, access requests, and configuration issues related to AI platforms in partnership with IT infrastructure, vendors, and business owners.
- Support governance activities including AI tool intake, risk/compliance reviews, and maintenance of an AI use\-case inventory.
- Coordinate user acceptance testing (UAT), release notes, and training materials for AI solution updates and rollouts.
GEO Monitoring \& Analysis
- Conduct regular (weekly/monthly) monitoring of how company brands, products, pipeline assets, and scientific/medical content appear across generative AI search and answer engines.
- Track accuracy, completeness, sentiment, and citation sources of AI\-generated responses referencing company assets; flag misinformation, outdated data, or off\-label/non\-compliant representations for immediate escalation.
- Benchmark company visibility in AI\-generated answers against competitors and industry standards; maintain a GEO scorecard/dashboard of findings and trends over time.
- Stay current on GEO/AI search best practices (structured data, content authority signals, schema markup, source citation patterns, llms.txt and similar emerging standards) and evaluate their applicability to company digital assets.
- Partner with IT and analytics teams to build or maintain lightweight tools, scripts, or dashboards that automate GEO monitoring where feasible.
Content Strategy \& Updates
- Translate GEO monitoring findings into concrete, prioritized recommendations for content updates across owned digital properties (websites, HCP/patient portals, press releases, product pages, structured data).
- Partner with Marketing, Corporate Communications, Medical Affairs, and Regulatory/Legal to route recommended content changes through appropriate medical\-legal\-regulatory (MLR) review and approval workflows.
- Support implementation of approved content updates in collaboration with web/content teams, ensuring changes are applied consistently across channels and properly tagged/structured for machine readability.
- Maintain documentation of GEO findings, actions taken, and outcomes to demonstrate impact and support continuous improvement.
Cross\-Functional Collaboration \& Reporting
- Prepare regular reports and presentations summarizing AI solution performance and GEO monitoring insights for IT leadership and business stakeholders.
- Act as a liaison between IT, Marketing/Digital, Medical, and Regulatory/Compliance functions on matters involving AI tools and public\-facing content accuracy.
- Contribute to the development of internal standards, playbooks, and SOPs governing GEO monitoring and AI solution support.
Qualifications:
Required Qualifications
- Bachelor’s degree in information technology, Business, Computer Science, Marketing, Communications, or a related field (or equivalent experience).
- 8\-10\+ years of experience as a Business Analyst, IT Analyst, Digital Analyst, or similar hybrid technical/business role in a life sciences, pharma, or biotech commercial, medical, or healthcare organization.
- Demonstrated experience supporting or administering software/SaaS platforms, including gathering requirements, coordinating testing, and managing stakeholder communication.
- Familiarity with generative AI tools and platforms (e.g., ChatGPT, Perplexity, Copilot, Gemini) and a genuine interest in how AI is changing search and content discovery.
- Conceptual understanding of comprehensive AI Tech Stack: Demonstrate conceptual understanding and practical application of RAG (Retrieval\-Augmented Generation), Graph RAG, Large Language Models (LLMs), Generative Engine Optimization (GEO), AI Search Optimization (AISO), Answer Engine Optimization (AEO), NLP, Predictive Analytics, Multi\-Hop Retrieval, Entity\-Based SEO, Knowledge Graphs, Semantic Structures, Vector Embeddings, Prompt Engineering, Intent Analysis, Data Chunking, etc.
- Working knowledge of SEO fundamentals and emerging GEO/AEO (Answer Engine Optimization) concepts, such as structured data, schema.org markup, content authority, and citation optimization.
- Strong analytical skills with the ability to synthesize monitoring data into clear, actionable insights for non\-technical stakeholders.
- Excellent written and verbal communication skills; comfortable presenting findings to cross\-functional and leadership audiences.
- Experience working within a regulated industry (life sciences, pharmaceutical, biotech, or healthcare) with an understanding of MLR/promotional review processes, or strong willingness and ability to learn.
Preferred Qualifications* Experience with content management systems (CMS), digital asset management (DAM), or web analytics platforms (e.g., Adobe Analytics, Google Analytics, Sitecore, AEM).
- Familiarity with AI/LLM monitoring or brand\-visibility tools, or experience building custom tracking using APIs, Python, or low\-code automation tools.
- Understanding of pharmaceutical promotional and medical communication compliance requirements (e.g., FDA OPDP guidance, PhRMA Code).
- Experience with project management or ticketing tools (e.g., Jira, ServiceNow, Azure DevOps).
*Please note: This position is classified as a corporate office position. In accordance with the New York state employer convenience rule, Stemline will withhold New York state income taxes for all corporate office employees, regardless of whether these employees work in New York or remotely.*
*Base Salary Range of $160000\-190000\. Menarini Stemline offers generous compensation and benefits packages, including, Fidelity 401(k) (with company match), Anthem Premier PPO and HDHP insurance plans, Company paid Basic Life \& AD\&D insurance and pre\-tax FSA/HSA programs.*
*Menarini Stemline is committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. Reasonable accommodations may be made to enable qualified individuals with disabilities to perform the essential functions.*
The Company
Founded in 1886 in Naples under the name of Farmacia Internazionale, Menarini moved in 1915 to Florence where the Group’s headquarters are still located today. High quality therapeutics and diagnostics solutions for patients, ethics as our underlying principle, dedication to innovation and advancement, strong people centricity and environmental sustainability. These five pillars form the foundation of the Menarini Group, an Italian pharmaceutical company with nearly 135 years of history. The Menarini Group is present in 70 countries and our products are sold in 140 locations around the world. Its companies span from Europe to Asia, to Africa and the Middle East, to Central America and to the United States where with the acquisition of Stemline Therapeutics, a NASDAQ\-listed biopharmaceutical company, the company marked its entry into the US oncology market.
Thanks to the valuable contribution of around 18,000 employees, every year more than 500 million packs of drugs are produced at the Group's 18 manufacturing sites including a biotech plant for the manufacturing of monoclonal antibodies that also serves external clients distributed across 6 continents.
Menarini has made a strong commitment to oncology, investing in a pipeline of five investigational compounds for the treatment of a variety of haematological and solid tumours. The acquisition of Stemline Therapeutics in June 2020, further strengthened Menarini’s oncology portfolio, adding both commercial and clinical\-stage assets. Tagraxofusp is a novel, first in class targeted therapy for patients with blastic plasmacytoid dendritic cell neoplasm (BPDCN) and to date, the only approved treatment for BPDCN in the U.S. and EU, and the first and only approved CD123\-targeted therapy. Tagraxofusp is also being evaluated as both a single agent and in combination, in other CD123\+ indications, including acute myeloid leukaemia (AML), chronic myelomonocytic leukaemia (CMML), and myelofibrosis (MF).
Additionally, Menarini received exclusive rights to commercialise Selinexor for the treatment of oncology indications in the European Union and other European countries (including the United Kingdom), Latin America and other key countries. Menarini has signed an exclusive licensing agreement with Karyopharm Therapeutics for the rights to commercialise an innovative therapeutic option in Europe, Latin America, Turkey, Russia, and CIS countries. Selinexor is a first\-in\-class, oral Selective Inhibitor of Nuclear export compound for the treatment of hematologic cancers and solid tumours. It is already marketed in the US for multiple myeloma and is under development for solid tumour indications. Selinexor is registered in the EU for both early and late lines.
Menarini entered into a global licence agreement with Radius Health to complete the development of Elacestrant, an oral SERD in late\-stage Phase 3 development for hormone receptor\-positive advanced breast cancer. Following a successful phase 3 study, Menarini Stemline received FDA approval in January 2023 under priority review and successfully oversaw a strong launch in February to the US market with the EMA review process concluded positively in September 2023\.
It is an exciting time in the company’s development and an excellent opportunity for individuals joining us to contribute to building and shaping Menarini Stemline’s Oncology business.
Salary Context
This $160K-$190K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Menarini, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($175K) sits 19% below the category median. Disclosed range: $160K to $190K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Menarini AI Hiring
Menarini has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $190K - $190K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.